where \(\odot \)  denotes element-wise multiplication, and \(({V_c}^*,{J_c}^*)\)  are the ground-truth root-relative coordinates of vertices and joints in the camera space.
Ideally, the VisDB network makes more confident predictions on the clearly visible joints and vertices.
Hence, we see the visibility labels as prediction confidence and use them to weight the coordinate losses.
In addition, we apply a pose prior loss \(\mathcal {L}_{prior}\)  using a fitted Gaussian Mixture Model (GMM) provided by [1]}:
\(\mathcal {L}_{prior} = - \text{log} \Big ( \sum _i G_i(\theta ) \Big ) ,\) 
